Misfire detection apparatus, misfire detection system, and data analysis apparatus

ABSTRACT

A misfire detection apparatus includes storage devices that store first mapping data and second mapping data, and execution devices. The execution device executes a first acquisition process, a first calculation process, a second acquisition process, a second calculation process, a determination process of determining whether or not a calculation result of the second calculation process and a calculation result of the first calculation process match, a count process of counting the number of consecutive times when a determination is made in the determination process that there is mismatch, and a transmission process of transmitting, to an outside of the vehicle, data on the value of the second misfire variable corresponding to the maximum number of times among the numbers of consecutive times counted in the count process during a predetermined period and data on the second input data used for calculating the value of the second misfire variable.

INCORPORATION BY REFERENCE

The disclosure of Japanese Patent Application No. 2019-152136 filed onAug. 22, 2019 including the specification, drawings and abstract isincorporated herein by reference in its entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to a misfire detection apparatus, amisfire detection system, and a data analysis apparatus.

2. Description of Related Art

Japanese Unexamined Patent Application Publication No. 4-91348 (JP4-91348A) proposes an apparatus provided with a neural network whichreceives a rotation fluctuation amount as a rotation speed change amountand outputs a value indicating whether or not a misfire has occurred ineach of a plurality of cylinders of an internal combustion engine.

SUMMARY

In general, in order to enhance the reliability of a learned model whichhas been learned by machine learning, learning using training data invarious situations needs to be performed. However, before the learnedmodel is mounted on the vehicle, sufficient training data for varioussituations that may occur when the learned model is actually mounted onthe vehicle cannot always be obtained. When sufficient training datacannot be obtained, it is difficult to verify whether or not the neuralnetwork outputs a correct value in various situations when the neuralnetwork is mounted on a vehicle.

The present disclosure provides a misfire detection apparatus, a misfiredetection system, and a data analysis apparatus.

A first aspect of the present disclosure relates to a misfire detectionapparatus including a storage device and an execution device. Thestorage device stores first mapping data defining a first mapping forreceiving first input data based on a detection value of a crank anglesensor of an internal combustion engine mounted on a vehicle as an inputand outputting a value of a first misfire variable that is a variablerelated to the presence or absence of misfire of the internal combustionengine, and second mapping data defining a second mapping for receivingsecond input data based on the detection value of the crank angle sensoras an input and outputting a value of a second misfire variable that isa variable related to the presence or absence of misfire of the internalcombustion engine, and the second mapping data including data learned bymachine learning, and the execution device executes a first acquisitionprocess of acquiring the first input data, a first calculation processof calculating a value of the first misfire variable by using the firstinput data acquired in the first acquisition process as an input of thefirst mapping, a second acquisition process of acquiring the secondinput data, a second calculation process of calculating a value of thesecond misfire variable by using the second input data acquired in thesecond acquisition process as an input of the second mapping, adetermination process of determining whether or not a calculation resultof the second calculation process and a calculation result of the firstcalculation process match, a count process of counting the number ofconsecutive times when a determination is made in the determinationprocess that there is mismatch, and a transmission process oftransmitting, to an outside of the vehicle, data on the value of thesecond misfire variable corresponding to the maximum number of timesamong the numbers of consecutive times counted in the count processduring a predetermined period and data on the second input data used forcalculating the value of the second misfire variable.

According to the first aspect, data on the value of the second misfirevariable when determination is made that the calculation result of thesecond calculation process and the calculation result of the firstcalculation process do not match, and data on the second input data usedfor calculating the value of the second misfire variable are transmittedto the outside of the vehicle. This transmission process is executed bythe misfire detection apparatus mounted on the vehicle, so that data invarious situations can be transmitted. Therefore, according to the firstaspect, it is possible to verify whether or not the value of the secondmisfire variable in various situations is correct, outside the vehicle.

However, for example, when determination is made that the calculationresult of the second calculation process and the calculation result ofthe first calculation process do not match, in the case of consecutivemismatches among the case of a single mismatch and the case ofconsecutive mismatches, there is a high probability that there is adifference in reliability between the first mapping and the secondmapping. Therefore, compared with the data on the second input data atthat time when match is determined once, the data on the second inputdata at that time when consecutive determinations are made that there ismismatch tends to include more useful information for verifying thereliability of the second mapping. Therefore, according to the firstaspect, the data on the value of the second misfire variable when thenumber of consecutive times when a determination is made that there ismismatch is the maximum number of times and the data on the second inputdata used for calculating the value are transmitted. As a result, it ispossible to transmit useful information while reducing the amount oftransmission data as compared with the case of transmitting all data onthe second input data corresponding to the case where a determination ismade that there is mismatch within the predetermined period.

In the misfire detection apparatus according to the first aspect, thetransmission process may include: a process of transmitting data on themaximum number of times, in addition to data on a predetermined numberof value among values of the second misfire variable corresponding tothe maximum number of times and data on the second input data used forcalculating each of the values.

According to the first aspect, even when the value of the second misfirevariable corresponding to the maximum number of times exceeds thepredetermined number, by limiting the value to be transmitted among thevalues of the second misfire variable when a determination is made thatthere is mismatch to a predetermined number, the amount of transmissiondata can be reduced. By further transmitting the data regarding themaximum number of times, when the value of the second misfire variablecorresponding to the maximum number of times exceeds a predeterminednumber, as compared with the case where the data regarding the maximumnumber of times is not transmitted, more detailed information to specifythe occurring situation can be provided.

In the misfire detection apparatus according to the first aspect, thesecond input data may be a rotation waveform variable that is a variableincluding information about a difference between the values of differentinstantaneous speeds at angular intervals, which are the rotation speedof the crankshaft of the internal combustion engine at angular intervalssmaller than the appearance interval of the compression top dead centerof the internal combustion engine; data on the second input data usedfor calculating the value of the second misfire variable may be aninstantaneous speed variable which is a variable indicating theinstantaneous speed at each of the angular intervals includinginformation about a difference between the instantaneous speedsindicated by the rotation waveform variable used for calculating thevalue of the second misfire variable when a determination is made thatthere is mismatch in the determination process; and the data to betransmitted in the transmission process may include the instantaneousspeed variable in each of the angular intervals generated before andafter the angular interval in time series, including information on thedifference between the instantaneous speeds indicated by the rotationwaveform variable used for the calculation.

According to the first aspect, the instantaneous speed variablecorresponding to the rotation waveform variable used for calculating thevalue of the second misfire variable, as well as the instantaneous speedvariables before and after the instantaneous speed variable in timeseries are transmitted in the transmission process. Thereby, as comparedwith the case where solely the instantaneous speed variablecorresponding to the rotation waveform variable used for calculating thevalue of the second misfire variable is transmitted, more detailedinformation about the rotation behavior of the crankshaft can beprovided outside the vehicle.

In the misfire detection apparatus according to the first aspect, theangular interval may be a second interval, the rotation waveformvariable may be time-series data configured with a variable thatindicates a difference between the instantaneous speed variables by theinstantaneous speed variable at each of the consecutive second intervalsincluded in the first interval larger than the second interval, and thedata to be transmitted in the transmission process may include theinstantaneous speed variable at each of the second intervals adjacent tothe first interval, in addition to the instantaneous speed variable ateach of the consecutive second intervals included in the first intervalwhen determination is made that there is mismatch in the determinationprocess.

According to the first aspect, since the instantaneous speed variable ineach of the consecutive second intervals for both the first interval andthe interval adjacent thereto is transmitted in the transmissionprocess, more detailed information on the rotation behavior of thecrankshaft can be provided as compared with the case where solely theinstantaneous speed variable at intermittent intervals is transmitted.

In the misfire detection apparatus according to the first aspect, thedata to be transmitted in the transmission process may include theinstantaneous speed variable when a determination is made that there ismismatch in the determination process, in addition to the instantaneousspeed variable relating to the rotation waveform variable used forcalculating the value of the second misfire variable when adetermination is made in the determination process that there is match.

According to the first aspect, the instantaneous speed variable whenmatch is determined is set as the transmission target, so that it iseasier to determine when the rotation behavior of the crankshaft doesnot match, as compared with the case where solely the instantaneousspeed variable when a determination is made that there is mismatch istransmitted.

In the misfire detection apparatus according to the first aspect, thedata to be transmitted in the transmission process may include theinstantaneous speed variable when a determination is made that there ismismatch in the determination process, and the instantaneous speedvariable in a state where a determination is made that there is match ata time of transition from a state where a determination is made thatthere is mismatch to the state where a determination is made that thereis match in the determination process.

According to the first aspect, at the time of transition from a statewhere a determination is made that there is mismatch to the state wherea determination is made that there is match in the determinationprocess, the instantaneous speed variable in the state where adetermination is made that there is match is to be transmitted. Thus,information on the rotation behavior of the crankshaft before and afterreturning from a state of mismatch to a state of match can be provided,so that it is easier to determine when the rotation behavior of thecrankshaft does not match, as compared with the case where solely theinstantaneous speed variable when mismatch is determined is transmitted.

In the misfire detection apparatus according to the first aspect, theexecution device may set a time interval between a pair of end timesadjacent to each other in time series among end times of travel of thevehicle to the predetermined period, and executes the transmissionprocess at the end time of the travel of the vehicle.

According to the first aspect, by executing the transmission process atthe end time of travel of the vehicle, the calculation load of themisfire detection apparatus during the traveling of the vehicle isreduced, as compared with the case where the transmission process isexecuted during the traveling of the vehicle.

A second aspect of the present disclosure relates to a misfire detectionsystem including the storage device, a first execution device, and asecond execution device outside the vehicle. The storage device storesfirst mapping data defining a first mapping for receiving first inputdata based on a detection value of a crank angle sensor of an internalcombustion engine mounted on a vehicle as an input and outputting avalue of a first misfire variable that is a variable related to thepresence or absence of misfire of the internal combustion engine, andsecond mapping data defining a second mapping for receiving second inputdata based on the detection value of the crank angle sensor as an inputand outputting a value of a second misfire variable that is a variablerelated to the presence or absence of misfire of the internal combustionengine, and the second mapping data including data learned by machinelearning, and the first execution device is a first execution devicemounted on the vehicle, and executes a first acquisition process ofacquiring the first input data, a first calculation process ofcalculating a value of the first misfire variable by using the firstinput data acquired in the first acquisition process as an input of thefirst mapping, a second acquisition process of acquiring the secondinput data, a second calculation process of calculating a value of thesecond misfire variable by using the second input data acquired in thesecond acquisition process as an input of the second mapping, adetermination process of determining whether or not a calculation resultof the second calculation process and a calculation result of the firstcalculation process match, a count process of counting the number ofconsecutive times when a determination is made that there is mismatch inthe determination process that the calculation results do not match, anda transmission process of transmitting, to an outside of the vehicle,data on the value of the second misfire variable corresponding to themaximum number of times among the numbers of consecutive times countedin the count process during a predetermined period and data on thesecond input data used for calculating the value of the second misfirevariable, and the second execution device outside the vehicle executes areception process of receiving the data transmitted in the transmissionprocess, a relearning data generation process for generating relearningdata that is data for relearning the second mapping, based on the datareceived by the reception process, and a relearning process ofrelearning the second mapping data, based on the relearning datagenerated in the relearning data generation process.

According to the second aspect, since the second mapping data can berelearned based on the input data to the second mapping when adetermination is made that there is mismatch, the second mapping can beconfigured to output a value with high accuracy in various situations ofthe vehicle. The fact that the second execution device is “outside thevehicle” means that the second execution device is not an in-vehicledevice.

In the misfire detection system according to the second aspect, therelearning data generation process may include a display process ofdisplaying the data received in the reception process on a displayapparatus, a validity determination result capturing process ofcapturing information on whether or not there is an error in the outputvalue of the second mapping, and a process of generating data forupdating the second mapping data, based on the information captured inthe validity determination result capturing process.

According to the second aspect, by displaying information such as inputdata to the second mapping transmitted in the transmission process onthe display apparatus, the validity of the output of the second mappingcan be examined by an entity that can determine the presence or absenceof a misfire from information on the second input data, in addition tothe first mapping and the second mapping. Then, by the entity capturingthe determination result by the validity determination result capturingprocess, it is possible to determine whether the input data to bedisplayed should be relearning data for updating the second mappingdata.

In the misfire detection system according to the second aspect, thestorage device may include a first storage device mounted on the vehicleand a second storage device outside the vehicle, wherein the secondstorage device stores third mapping data defining a third mapping forreceiving third input data based on a detection value of the crank anglesensor as an input and outputting a value of a third misfire variablethat is a variable relating to the presence or absence of a misfire ofthe internal combustion engine, and the relearning data generationprocess includes a third calculation process of inputting the datareceived by the reception process to the third mapping and calculates avalue of the third misfire variable, and a process of generating datafor updating the second mapping data, based on whether or not thecalculation result of the third calculation process and the calculationresult of the second calculation process match.

According to the second aspect, the validity of the second output valuecan be verified by determining whether or not the third output value andthe second output value match, and whether or not the data is to be usedfor relearning can be determined. The fact that the second storagedevice is “outside the vehicle” means that the second storage device isnot an in-vehicle device.

A third aspect of the present disclosure relates to a data analysisapparatus including an execution device outside a vehicle. The executiondevice executes a reception process of receiving data on the value ofthe misfire variable corresponding to the maximum number of consecutivetimes among the numbers of consecutive times counted in thepredetermined period, transmitted from the vehicle, and data on inputdata based on the detection value of the crank angle sensor used tocalculate the value of the misfire variable, a relearning datageneration process of generating relearning data that is data forrelearning a mapping, based on the data received by the receptionprocess, and a relearning process of relearning the mapping data, basedon the relearning data generated in the relearning data generationprocess, wherein the mapping is a mapping defined to receive input databased on a detection value of a crank angle sensor as an input andoutput a value of a misfire variable that is a variable related to thepresence or absence of a misfire of the internal combustion engine.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, advantages, and technical and industrial significance ofexemplary embodiments of the present disclosure will be described belowwith reference to the accompanying drawings, in which like signs denotelike elements, and wherein:

FIG. 1 is a diagram illustrating a configuration of a learning controlsystem according to a first embodiment;

FIG. 2 is a flowchart illustrating a procedure of a process executed bya control device according to the first embodiment;

FIG. 3 is a flowchart illustrating a procedure of the process executedby the control device according to the first embodiment;

FIG. 4 is a flowchart illustrating a procedure of a process executed bythe system according to the first embodiment;

FIG. 5 is a view illustrating transmission data according to the firstembodiment;

FIG. 6 is a time chart illustrating a transition of a maximum value C0according to the first embodiment;

FIG. 7 is a diagram illustrating a configuration of a learning controlsystem according to a second embodiment;

FIG. 8 is a flowchart illustrating a procedure of a process executed bya control device according to the second embodiment;

FIG. 9 is a flowchart illustrating a procedure of the process executedby the control device according to the second embodiment; and

FIG. 10 is a flowchart illustrating a procedure of a process executed bythe system according to the second embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS First Embodiment

Hereinafter, a first embodiment of a misfire detection system will bedescribed with reference to the drawings. In an internal combustionengine 10 mounted on a vehicle VC1 shown in FIG. 1, a throttle valve 14is provided in an intake passage 12. The air sucked from the intakepassage 12 flows into combustion chambers 18 of cylinders #1 to #4 whenan intake valve 16 opens. Fuel is injected into the combustion chamber18 by a fuel injection valve 20. In the combustion chamber 18, anair-fuel mixture of air and fuel is provided for combustion by sparkdischarge of an ignition device 22, and energy generated by thecombustion is taken out as rotational energy of a crankshaft 24. Theair-fuel mixture supplied for the combustion is discharged to an exhaustpassage 28 as exhaust, with the opening of the exhaust valve 26. Acatalyst 30 having oxygen storage capacity is provided in the exhaustpassage 28.

An input shaft 56 of a transmission 54 can be connected to thecrankshaft 24 of the internal combustion engine 10 via a torqueconverter 50. The torque converter 50 includes a lock-up clutch 52, andthe crankshaft 24 and the input shaft 56 are connected when the lock-upclutch 52 enters the engagement state. A drive wheel 60 is mechanicallyconnected to an output shaft 58 of the transmission 54.

The crankshaft 24 is coupled to a crank rotor 40 provided with teeth 42indicating each of a plurality of rotation angles of the crankshaft 24.In the present embodiment, 34 teeth 42 are illustrated. Basically, thecrank rotor 40 is provided with the teeth 42 at 10° C.A intervals, butone missing tooth 44 where the interval between adjacent teeth 42 is 30°C.A is provided. This is to indicate the reference rotation angle of thecrankshaft 24.

The control device 70 operates the operation units of the internalcombustion engine 10, such as the throttle valve 14, the fuel injectionvalve 20, and the ignition device 22, in order to control the torque,the exhaust component ratio, or the like which are control amounts ofthe internal combustion engine 10 as an object to be controlled. Thecontrol device 70 operates the lock-up clutch 52 in order to control theengagement state of the lock-up clutch 52, which is a control amount ofthe torque converter 50 as an object to be controlled. The controldevice 70 operates the transmission 54 to control a gear ratio, which isa control amount of the transmission 54 to be controlled. FIG. 1 showsoperation signals MS1 to MS5 of the throttle valve 14, the fuelinjection valve 20, the ignition device 22, the lock-up clutch 52, andthe transmission 54, respectively.

When controlling the control amount, the control device 70 refers to anoutput signal Scr of the crank angle sensor 80 which outputs a pulse foreach angular interval between the teeth 42 provided at every 10° C.Aexcept for the missing tooth 44, and the intake air amount Ga detectedby an air flow meter 82. The control device 70 refers to the coolanttemperature THW which is the temperature of the coolant of the internalcombustion engine 10 detected by the coolant temperature sensor 84, theoutside air temperature Ta detected by the outside temperature sensor86, and the shift position Vsft of the transmission 54 detected by theshift position sensor 88.

The control device 70 includes a CPU 72, a ROM 74, a storage device 76which is an electrically rewritable nonvolatile memory, a communicationdevice 77, and a peripheral circuit 78, which can communicate with eachother via a local network 79. The peripheral circuit 78 includes acircuit that generates a clock signal that defines an internaloperation, a power supply circuit, a reset circuit, or the like. Thestorage device 76 stores practical mapping data 76 a and evaluationmapping data 76 b. Here, the practical mapping data 76 a is dataactually used for monitoring misfire of the internal combustion engine10. On the other hand, the evaluation mapping data 76 b is data of whichreliability is to be evaluated, and is not used for monitoring misfireof the internal combustion engine 10. The evaluation mapping data 76 bis mounted on the control device 70 after learning to some extent bymachine learning.

The control device 70 controls the control amount by causing the CPU 72to execute a program stored in the ROM 74. Specifically, the ROM 74stores a misfire detection program 74 a and a relearning subprogram 74b. Here, the relearning subprogram 74 b is a program for executingrelearning of the evaluation mapping data 76 b.

The communication device 77 is a device for communicating with the dataanalysis center 100 via the network 110 outside the vehicle VC1. Thedata analysis center 100 analyzes data transmitted from the vehiclesVC1, VC2, . . . . The data analysis center 100 includes a CPU 102, a ROM104, a storage device 106, a communication device 107, and a peripheralcircuit 108, and these can be communicated via a local network 109. TheROM 104 stores a relearning main program 104 a that defines a process ofgenerating data for relearning the evaluation mapping data 76 b, basedon data transmitted from the vehicles VC1, VC2, . . . . The storagedevice 106 stores relearning data 106 a transmitted from a plurality ofvehicles VC1, VC2, . . . for relearning the mapping defined by theevaluation mapping data 76 b.

FIG. 2 shows a part of a process realized by the CPU 72 executing themisfire detection program 74 a stored in the ROM 74. The process shownin FIG. 2 is a process using the practical mapping data 76 a. Theprocess shown in FIG. 2 is repeatedly executed, for example, at apredetermined cycle. In the following, the step number of each processis represented by a number prefixed with “S”.

In the series of processes shown in FIG. 2, the CPU 72 first acquires aminute rotation time T30 (S10). The minute rotation time T30 is a timefor the crankshaft 24 to rotate by 30° C.A, and is calculated by the CPU72 based on the output signal Scr of the crank angle sensor 80. Next,the CPU 72 sets the latest minute rotation time T30 acquired in theprocess of S10 to the minute rotation time T30[0], and sets the variable“m” of the minute rotation time T30[m] to a larger value as the value isearlier (S12). That is, assuming that “m 1, 2, 3, . . . ”, the minuterotation time T30[m−1] immediately before the process of S12 isperformed is set to the minute rotation time T30[m]. Thereby, forexample, the minute rotation time T30 acquired by the process of S10when the process of FIG. 2 is executed last time becomes the minuterotation time T30[1]. Among the minute rotation times T30[0], T30[1],T30[2], . . . , the minute rotation times T30 that are adjacent to eachother in time series indicate times for rotation by an angular intervalof 30° C.A adjacent to each other, and the angular intervals do not haveoverlapping parts.

Next, the CPU 72 determines whether or not the minute rotation time T30acquired in the process of S10 is time for rotation by an angularinterval from 30° C.A before the compression top dead center of any ofthe cylinders #1 to #4 to the compression top dead center (S14). In acase where the CPU 72 determines that it is the time for the rotation bythe angular interval up to the compression top dead center (S14. YES),in order to determine the presence or absence of misfire in the cylinderat the compression top dead center, the CPU 72 puts “T30[0]-T30[6]” intothe rotation fluctuation amount Δω(i) of the cylinder #i to bedetermined (S16). That is, the rotation fluctuation amount Δω isquantified by subtracting the time for rotation by an angular intervalfrom 30° C.A before the compression top dead center to the compressiontop dead center of the preceding cylinder of the cylinder to besubjected to misfire determination to the compression top dead center,from the time for rotation by an angular interval from 30° C.A beforethe compression top dead center to the compression top dead center ofthe cylinder to be subjected to misfire determination.

Next, the CPU 72 determines whether or not the rotation fluctuationamount Δω(i) is equal to or larger than the specified amount Δωth (S18).This process is a process of determining whether or not a misfire hasoccurred in a cylinder to be subjected to misfire determination. Here,the CPU 72 variably sets the specified amount Δωth, based on therotation speed NE and the charging efficiency η.

More specifically, the CPU 72 performs a map calculation of thespecified amount Δωth in a state where map data using the rotation speedNE and the charging efficiency η as input variables and the specifiedamount Δωth as an output variable is stored in the storage device 76 inadvance. The map data is set data of discrete values of the inputvariables and the values of the output variables corresponding to thevalues of the input variables. For example, the map calculation may be aprocess in which the values of the output variables of the correspondingmap data is the calculation result, when the value of the input variablematches any one of the values of the input variables in the map data,and values obtained by interpolating values of a plurality of outputvariables included in map data are the calculation results, in a case ofmismatch.

Incidentally, the rotation speed NE is calculated by the CPU 72 based onthe output signal Scr of the crank angle sensor 80. Here, the rotationspeed NE is an average value of the rotation speed when the crankshaft24 rotates by an angular interval larger than the appearance interval ofthe compression top dead center (180° C.A in the present embodiment).The rotation speed NE is desirably an average value of the rotationspeed when the crankshaft 24 rotates by the rotation angle of one ormore rotations of the crankshaft 24. The average value here is notlimited to the simple average, but may be, for example, an exponentialmoving average, or in short, a value in which the low-frequencycomponent is obtained by removing the higher-order component fluctuatingas much as the appearance interval of the compression top dead center.The charging efficiency η is calculated by the CPU 72 based on therotation speed NE and the intake air amount Ga.

The process of S16 and S18 is a process using the practical mapping data76 a. That is, the practical mapping data 76 a defines a mapping ofreceiving the minute rotation time T30[0] and the minute rotation timeT30[6] and outputting a logical value corresponding to the occurrence ofmisfire in the cylinder to be determined as an output value. The logicalvalue here is a value related to whether the proposition that therotation fluctuation amount Δω(i) is equal to or greater than thespecified amount Δωth is true or false.

When determining that the rotation fluctuation amount Δω(i) is equal toor greater than the specified amount Δωth (S18: YES), the CPU 72determines that a misfire has occurred in the cylinder #i (S20). Next,the CPU 72 increments the misfire counter CN(i) of the cylinder #i(S22). Then, the CPU 72 determines whether or not the logical sum of thefacts that a predetermined period has elapsed since the process of S18is first performed in a state where the misfire counter CN(i) has beeninitialized and that a predetermined period has elapsed since theprocess of S28 described below is performed is true (S24). Whendetermining that the logical sum is true (S24: YES), the CPU 72determines whether or not the misfire counter CN(i) is equal to orlarger than the threshold CNth (S26). When determining that the value isless than the threshold value CNth (S26: NO), the CPU 72 initializes themisfire counter CN(i) (S28).

On the other hand, in a case where the CPU 72 determines that thedifference is equal to or larger than the threshold CNth (S26: YES), theCPU 72 operates the warning light 90 shown in FIG. 1 to notify the userthat an abnormality has occurred (S30). The CPU 72 temporarily ends theseries of processes shown in FIG. 2 once, when the processes of S28 andS30 are completed, or when a negative determination is made in theprocesses of S14 and S24.

FIG. 3 shows a partial procedure of a process realized by the CPU 72executing the misfire detection program 74 a stored in the ROM 74. Theprocess shown in FIG. 3 is a process using the evaluation mapping data76 b.

In the series of processes shown in FIG. 3, the CPU 72 first acquiresthe minute rotation times T30(1), T30(2), . . . T30(24), the rotationspeed NE, and the charging efficiency η (S40). Here, the minute rotationtimes T30(1), T30(2), . . . are different from the minute rotation timesT30[1], T30[2], . . . illustrated in FIG. 2. In particular, the minuterotation times T30(1), T30(2), . . . have later values as the numbers inparentheses are larger. The minute rotation times T30(1) to T30(24) arerotation times at 24 angular intervals obtained by equally dividing therotation angle region of 720° C.A by 30° C.A.

Next, the CPU 72 puts the values obtained by the process of S40 into theinput variables x(1) to x(26) of the mapping defined by the evaluationmapping data 76 b (S42). More specifically, the CPU 72 sets “s=1 to 24”and puts the minute rotation time T30(s) for the input variable x(s).That is, the input variables x(1) to x(24) are time-series data of theminute rotation time T30. The CPU 72 puts the rotation speed NE for theinput variable x(25) and puts the charging efficiency η for the inputvariable x(26).

Next, the CPU 72 calculates the values of the misfire variables P(1) toP(5) by inputting the input variables x(1) to x(26) into the mappingdefined by the evaluation mapping data 76 b (S44). Here, assuming that“i=1 to 4”, the misfire variable P(i) is a variable having a largervalue when the probability of the occurrence of misfire in the cylinder#i is higher than when the probability is lower. The misfire variableP(5) is a variable having a larger value when the probability of theoccurrence of no misfire in any of the cylinders #1 to #4 is higher thanwhen the probability is lower.

More specifically, the mapping defined by the evaluation mapping data 76b is a neural network having a single intermediate layer. The neuralnetwork described above has coefficients w(1)ji (j=0 to n, i=0 to 26)and an activation function h1(x) which is non-linear mapping fornon-linear conversion of the output of linear mapping defined by thecoefficients w(1)ji. In the present embodiment, a hyperbolic tangent isexemplified as the activation function h1(x). Incidentally, w(1)j 0 orthe like is a bias parameter, and the input variable x(0) is defined as“1”.

The neural network includes a softmax function for receivingcoefficients w(2)kj (k=1 to 5, j=0 to n) and prototype variables y(1) toy(5) which are outputs of a linear mapping defined by the coefficientw(2)kj as inputs, and outputting misfire variables P(1) to P(5).

Next, the CPU 72 specifies the largest one among the misfire variablesP(1) to P(5) (S46). Then, the CPU 72 determines whether the maximummisfire variable P(q) is any of the misfire variables P(1) to P(4) orthe misfire variable P(5) (S48). When determining that the maximummisfire variable P(q) is any of the misfire variables P(1) to P(4) (S48:YES), the CPU 72 determines that misfire occurs in the cylinder #q(S50).

The CPU 72 temporarily ends the series of processes shown in FIG. 3once, when the process of S50 is completed, or when a negativedetermination is made in the process of S48. FIG. 4 shows a procedure ofa process related to relearning of the evaluation mapping data 76 baccording to the present embodiment. The process shown on the left sideof FIG. 4 is realized by the CPU 72 executing the relearning subprogram74 b stored in the ROM 74 shown in FIG. 1. The process shown on theright side of FIG. 4 is realized by the CPU 102 executing the relearningmain program 104 a stored in the ROM 104. Hereinafter, the processillustrated in FIG. 4 will be described along the time series of therelearning process.

In the series of processes shown on the left side of FIG. 4, the CPU 72first determines whether or not it is the reliability verificationperiod of the evaluation mapping data 76 b (S60). Specifically, in thepresent embodiment, the following periods are set as verificationperiods.

(A) A period when the coolant temperature THW is equal to or lower thana predetermined temperature:

When the coolant temperature THW is low, the combustion tends to beunstable, and it is difficult to improve the misfire detection accuracyas compared with the case where the coolant temperature THW is high, sothat this period is included in the verification period.

(B) A period when the outside air temperature Ta is equal to or lowerthan a defined temperature:

When the outside air temperature Ta is low, the combustion tends to beunstable, and it is difficult to improve the misfire detection accuracyas compared with the case where the outside air temperature Ta is high,so that this period is included in the verification period.

(C) Execution period of the warm-up process of the catalyst 30:

During the execution period of the warm-up process of the catalyst 30,since the combustion is performed with reduced combustion efficiency,the combustion tends to be unstable, and it is difficult to improve themisfire detection accuracy than after the catalyst 30 is warmed up, sothat this period is included in the verification period.

(D) A period when the charging efficiency η is equal to or less than apredetermined value:

When the load is light, the combustion tends to be unstable than whenthe load is heavy, and it is difficult to improve the misfire detectionaccuracy than when the load is medium or high, so that this period isincluded in the verification period.

(E) A period in which the amount of change ΔNE in the rotation speed NEper predetermined time is equal to or more than a predetermined value:

During the transient operation, the misfire detection accuracy tends tobe lower than in the steady operation, so that this period is includedin the verification period.

When determination is made that it is the verification period (S60:YES), the CPU 72 determines whether or not the flag F is “1” (S62).Here, the flag F becomes “1” when the misfire determination resultobtained by the process shown in FIG. 2 and the misfire determinationresult obtained by the process shown in FIG. 3 do not match, and theflag F becomes “0” when the misfire determination results match. Whendetermination is made that the flag F is “0” (S62: NO), the CPU 72determines whether or not the misfire determination result by theprocess shown in FIG. 2 does not match the misfire determination resultobtained by the process shown in FIG. 3 (S64). The CPU 72 determinesthat there is mismatch when four determination results obtained by theprocess of step S18 of FIG. 2 in the same combustion cycle do not matchthe result of the process of step S46 in FIG. 3. That is, P(5) isselected in the process of S46, for example, although determination ismade in the process of S18 that the rotation fluctuation amount Δω(1) ofthe cylinder #1 is equal to or larger than the specified amount Δωth,the CPU 72 determines that the results do not match.

When determining that the results do not match (S64: YES), the CPU 72puts “1” for the flag F (S66). Next, the CPU 72 increments the counter C(S68). On the other hand, when determination is made that the flag F is“1” (S62: YES), the CPU 72 determines whether or not the misfiredetermination result by the process shown in FIG. 2 and the misfiredetermination result obtained by the process shown in FIG. 3 match(S70). When determining that the results do not match (S70: NO), the CPU72 proceeds to the process of S68, and when determining that the resultsmatch (S70: YES), the CPU 72 puts “0” into the flag F (S72). Then, theCPU 72 determines whether or not the counter C has a value larger thanthe maximum value C0 (S74). The maximum value C0 is the maximum numberof consecutive times in which determination is made that thedetermination result using the practical mapping data 76 a and thedetermination result using the evaluation mapping data 76 b do notmatch. When determination is made that the value is larger than themaximum value C0 (S74: YES), the CPU 72 updates the maximum value C0 tothe current value of the counter C, and updates the rotation time setGrT30 and the extra information set GrE (S76).

Specifically, the rotation time set GrT30 is a set of minute rotationtimes T30(1) to T30(72) for three combustion cycles, as shown in FIG. 5.However, the minute rotation times T30(49) to T30(72) is updated so asto correspond to the combustion cycle in which determination is made inthe latest process of S70 that the misfire determination result obtainedby the process shown in FIG. 2 matches the misfire determination resultobtained by the process shown in FIG. 3. Here, when the maximum value C0is equal to or more than “2”, the minute rotation times T30(1) toT30(24) and the minute rotation times T30(25) to T30(48) all correspondto the combustion cycle in which the misfire determination resultobtained by the process shown in FIG. 2 and the misfire determinationresult obtained by the process shown in FIG. 3 are different from eachother. The initial value of the maximum value C0 is zero.

The extra information set GrE includes a rotation speed NE, a chargingefficiency η, a warm-up control variable Vcat indicating whether or nota warm-up process of the catalyst 30 is performed, an outside airtemperature Ta, a coolant temperature THW, a shift position Vsft of thetransmission 54, and an engagement variable Vrc which is a variableindicating the engagement state of the lock-up clutch 52. It isdesirable that each of these variables is a value in the combustioncycle before the combustion cycle for which the affirmativedetermination has been made in the process of S70. The extra informationset GrE is a set of variables that affect the rotation behavior of thecrankshaft 24 according to the presence or absence of misfire, inaddition to the rotation speed NE and the charging efficiency η asoperating point variables which are inputs to the mapping defined by theevaluation mapping data 76 b. That is, since the inertia constant fromthe crankshaft 24 to the drive wheels 60 varies depending on theengagement state and the shift position Vsft of the lock-up clutch 52,the rotation behavior of the crankshaft 24 becomes different. Thewarm-up control variable Vcat, the outside air temperature Ta, and thecoolant temperature THW are variables indicating whether or not thecombustion state is stable.

Returning to FIG. 4, when the process of S76 is completed or when anegative determination is made in the process of S74, the CPU 72initializes the counter C (S79). Then, when the processes of S68 and S79are completed, or when a negative determination is made in the processesof S60 and S64, the CPU 72 determines whether or not it is the end timeof the trip (S78). Here, the trip is one period in which the travelingpermission signal of the vehicle is in the ON state. In the presentembodiment, the traveling permission signal corresponds to an ignitionsignal. When determining that it is the end time of the trip (S78: YES),the CPU 72 operates the communication device 77 to transmit information“q” regarding the largest one among the misfire variables P(1) to P(5),the maximum value C0, the rotation time set GrT30, and the extrainformation set GrE to the data analysis center 100 (S80).

As described above, in the present embodiment, once per trip, the minuterotation time T30 for two combustion cycles corresponding to the maximumvalue C0 and the minute rotation time T30 for one combustion cycleadjacent to the same combustion cycle are transmitted. This is becausewhen the number of consecutive times in which the determination resultusing the practical mapping data 76 a and the determination result usingthe evaluation mapping data 76 b do not match is large, compared tosingle-time mismatch, it is highly likely that useful information forverifying the reliability of the evaluation mapping data 76 b isincluded. Therefore, it is possible to transmit useful information whilereducing the amount of transmission data as compared with transmittingthe minute rotation time T30 of all the combustion cycles when adetermination is made that there is mismatch in one trip. The maximumvalue C0 is initialized at the start of a new trip.

On the other hand, as shown on the right side of FIG. 4, the CPU 102receives the information “q” regarding the largest one among the misfirevariables P(1) to P(5), the maximum value C0, the rotation time setGrT30, and the extra information set GrE (S90). The CPU 102 displays, onthe display apparatus 112 shown in FIG. 1, waveform data on the rotationbehavior of the crankshaft 24 represented by the rotation time setGrT30, and displays the information “q” regarding the largest one amongthe misfire variables P(1) to P(5), the maximum value C0, and the extrainformation set GrE (S92). This is a process of providing a skilledperson with information that allows the skilled person to determinewhether or not a misfire has occurred. That is, the skilled person candetermine with high accuracy whether or not a misfire has occurred byvisually recognizing the waveform data. At that time, by referring tothe information of the extra information set GrE, the determination asto whether or not a misfire has occurred becomes more reliable. Thereby,the skilled person can determine whether or not the misfiredetermination using the evaluation mapping data 76 b is an erroneousdetermination, based on the determination of whether or not a misfirehas occurred.

When a determination result is input by a skilled person operating theinterface 114 shown in FIG. 1, the CPU 102 acquires the determinationresult (S94). Then, the CPU 102 determines whether or not thedetermination result input by the operation of the interface 114 is adetermination that the misfire determination using the evaluationmapping data 76 b is an erroneous determination (S96). Whendetermination is that the determination is an erroneous determination(S96: YES), the CPU 102 stores at least the minute rotation timesT30(25) to T30(48), the rotation speed NE, and the charging efficiency11, and the result of the determination by a skilled person as towhether or not a misfire has occurred, among the data received in theprocess of S90, as the relearning data 106 a (S98). The relearning data106 a includes data based on data received from the vehicle VC1 as wellas other vehicles VC2, . . . equipped with an internal combustion enginehaving the same specifications as the internal combustion engine 10.

Next, the CPU 102 determines whether or not the relearning data 106 astored in the storage device 106 is equal to or larger than apredetermined amount (S100). When determination is made that the amountis equal to or larger than the predetermined amount (S100: YES), the CPU102 updates the coefficients w(1)ji, w(2)kj which are learned parametersof the evaluation mapping data 76 b, using the relearning data 106 a astraining data (S102). That is, the CPU 72 calculates the misfirevariables P(1) to P(5) by using as the input variables x(1) to x(26),data other than the data regarding the result of the determination bythe skilled person as to whether or not it is the misfire, among thetraining data, and generates teacher data based on the data on theresult of the determination by the skilled person as to whether or notit is misfire. For example, when the skilled person determines thatmisfire occurs in the cylinder #1, P(1)=1 and P(2) to P(5)=0. Forexample, when the skilled person makes a determination that it isnormal, P(1) to P(4)=0 and P(5)=1. Then, the coefficients w(1)ji, w(2)kjare updated by a known method such that the absolute value of thedifference between the teacher data and the misfire variables P(1) toP(5) output from the neural network becomes small (S102).

In the calculation process of the misfire variables P(1) to P(5),information on the coefficients w(1)ji and w(2)kj, and the informationindicating that the activation function h1, and the softmax function inthe output layer of the neural network are used is needed. Regardingthis, for example, when an affirmative determination is made in theprocess of S100, an instruction to transmit data on these may be outputfrom the CPU 102 to the control device 70, or may be stored in thestorage device 106 in advance, for example.

Then, the CPU 102 operates the communication device 107 to transmit theupdated coefficients w (1)ji, w(2)kj to the vehicles VC1, VC2, . . . asrelearned parameters (S104). When the process of S104 is completed, orwhen a negative determination is made in the processes of S96 and S100,the CPU 102 temporarily ends a series of processes illustrated on theright side of FIG. 4.

On the other hand, as shown on the left side of FIG. 4, the CPU 72determines whether or not there is transmission of a relearned parameterfrom the data analysis center 100 (S82). Then, when determination ismade that there is the relearned parameter (S82: YES), the CPU 102receives coefficients w(1)ji and w(2)kj (S84), and updates theevaluation mapping data 76 b stored in the storage device 76 (S86).

When the process of S86 is completed, or when a negative determinationis made in the processes of S78 and S82, the CPU 72 temporarily ends aseries of processes illustrated on the left side of FIG. 4. Here, theoperation and effect of the present embodiment will be described.

The CPU 72 monitors the presence or absence of a misfire of the internalcombustion engine 10 by executing the process shown in FIG. 2, based onthe practical mapping data 76 a, and when frequent misfire occurs, theCPU 72 executes a notification process to deal with the frequentoccurrence of the misfire. Based on the evaluation mapping data 76 b,the CPU 72 executes the process shown in FIG. 3 to execute a misfiredetermination based on the evaluation mapping data 76 b. Then, the CPU72 determines whether or not the misfire determination result using theevaluation mapping data 76 b matches the misfire determination resultusing the practical mapping data 76 a. When a determination is made thatthere is mismatch, the CPU 72 counts the number of times that themismatching state continuously appears, and calculates the maximum valueC0.

FIG. 6 shows the transition of the maximum value C0. In FIG. 6, “O”indicates a combustion cycle in which a misfire determination resultusing the evaluation mapping data 76 b and a misfire determinationresult using the practical mapping data 76 a are determined to match,and “x” indicates a combustion cycle determined to mismatch.

As shown in FIG. 6, during a period from time t1 to time 2 when themaximum value C0 is the initial value “0” before time t2, the number ofconsecutive combustion cycles when a determination is made that there ismismatch is three, and thereafter, when returning to the state where adetermination is made that there is match, the CPU 72 sets the maximumvalue C0 to “3”. Thereafter, during a period from time t3 to time t4,the number of consecutive combustion cycles where a determination ismade that there is mismatch is 14, and thereafter, when returning to thestate where a determination is made that there is match, the CPU 72updates the maximum value C0 to “14”.

Then, when the trip ends at time t5, the CPU 72 transmits input data forthree combustion cycles including the last two combustion cycles in theperiod when a determination is made that there are 14 consecutivemismatches, which corresponds to the maximum value C0, and onecombustion cycle when returning to the state where a determination ismade that there is match.

On the other hand, the CPU 102 displays the input data or the liketransmitted from the CPU 72 on the display apparatus 112. Thereby, theskilled person determines whether or not a misfire has occurred, basedon waveform data or the like indicating the rotation behavior of thecrankshaft 24, and based on this, determines whether or not thedetermination of the presence or absence of a misfire using theevaluation mapping data 76 b is an erroneous determination. When thedetermination result by the skilled person indicates that thedetermination as to the presence or absence of misfire using theevaluation mapping data 76 b is an erroneous determination, the CPU 102stores a part of the data transmitted from the vehicle side asrelearning data 106 a in the storage device 106. When the relearningdata 106 a becomes equal to or more than the predetermined amount, theCPU 102 updates the coefficients w(1)ji, w(2)kj and transmits therelearning data to each of the vehicles VC1, VC2, . . . .

Thus, in each of the vehicles VC1, VC2, . . . , the evaluation mappingdata 76 b is updated with the coefficients w(1)ji and w(2)kj updated byusing the data that causes the erroneous determination by using theevaluation mapping data 76 b in its own vehicle, as well as the datathat causes the erroneous determination by using the evaluation mappingdata 76 b in the other vehicles.

Therefore, the evaluation mapping data 76 b can be updated to data bywhich misfire can be determined in various situations with highaccuracy. In a case where determination is made that the evaluationmapping data 76 b has higher reliability by the skilled person'sdetermination when the mismatch occurs, the updated evaluation mappingdata 76 b can be used as the practical mapping data 76 a to monitormisfire. The learned model (mapping data) based on the raw datainstalled on the vehicles VC1, VC2, . . . can be installed as practicalmapping data from the beginning on a newly developed control devicemounted on a vehicle equipped with an internal combustion engine havingthe same number of cylinders.

According to the present embodiment described above, the followingeffects can be further obtained. (1) When mismatch occurs between thedetermination result based on the practical mapping data 76 a and thedetermination result based on the evaluation mapping data 76 b, theminute rotation times T30 (25) to T30 (48) in the mismatched combustioncycle, as well as the minute rotation times T30 (49) to T30 (72) in thecombustion cycle that has been restored from mismatch to match aretransmitted to the data analysis center 100. Thereby, the informationabout the state where the mismatch has occurred as well as theinformation at the time of transition to the state where the mismatchhas been resolved is transmitted. Therefore, as compared with the casewhere solely the minute rotation times T30 (25) to T30 (48), which arethe waveform data of one mismatched combustion cycle, are transmitted,the skilled person can more accurately determine whether or not misfirehas occurred.

(2) When mismatch occurs between the determination result based on thepractical mapping data 76 a and the determination result based on theevaluation mapping data 76 b, the extra information set GrE is alsotransmitted. Thus, as compared with the case where solely the minuterotation times T30 (1) to T30 (72), which are the waveform dataindicating the rotation behavior of the crankshaft 24, are transmitted,the skilled person can more accurately determine whether or not misfirehas occurred.

(3) When mismatch occurs between the determination result based on thepractical mapping data 76 a and the determination result based on theevaluation mapping data 76 b, the data when the mismatch occurs istransmitted to the data analysis center 100 at the end time of the trip.At the end time of the trip, since the calculation load of the controldevice 70 is smaller than when the vehicle is traveling, it is possibleto suppress the calculation load applied to the control device 70 frombeing excessively increased in the transmission process.

Second Embodiment

Hereinafter, the second embodiment will be described with reference tothe drawings, focusing on differences from the first embodiment.

FIG. 7 shows a configuration of a misfire detection system according tothe second embodiment. In FIG. 7, the same reference numbers are givento the members corresponding to the members shown in FIG. 1 forconvenience. The storage device 106 shown in FIG. 7 storeshigh-specification mapping data 106 b. In exchange for the large numberof dimensions of the input variables and the complexity of the mappingstructure, the high-specification mapping data 106 b can be used toperform misfire determination simulating a skilled person. In learningthe high-specification mapping data 106 b, the rotation time set GrT30and the extra information set GrE in the processing of FIG. 4 and thedetermination result of the skilled person by the processing of S94 andS96 are used as training data.

In the present embodiment, a case is shown in which the reliability ofthe evaluation mapping data 76 b is improved by the process of the firstembodiment, and the evaluation mapping data 76 b is implemented as thepractical mapping data 76 a. FIG. 8 shows a part of a process realizedby the CPU 72 executing the misfire detection program 74 a stored in theROM 74. The process shown in FIG. 8 is a process using the practicalmapping data 76 a. The process shown in FIG. 8 is repeatedly executed,for example, at a predetermined cycle. In FIG. 8, the same step numbersare given to the processes corresponding to the processes shown in FIGS.2 and 3 for convenience.

In a series of processes shown in FIG. 8, the CPU 72 executes processessimilar to the processes of S40 to S48 of FIG. 3. That is, in thisembodiment, since the evaluation mapping data 76 b used in the processof FIG. 3 is the practical mapping data 76 a, the processes of S40 toS48 are executed using the practical mapping data 76 a. In FIG. 8, thelargest one among misfire variables P(1) to P(5) is described as misfirevariable P(i), and is different from the misfire variable P(q) in FIG. 3in description, but the process itself is the same.

When an affirmative determination is made in the process of S48, the CPU72 executes the processes of S22 to S30 on the cylinder #i for whichmisfire is determined to have occurred, while when a negativedetermination is made in the process of S48, the CPU 72 executes theprocesses of S24 to S30.

FIG. 9 shows a partial procedure of a process realized by the CPU 72executing the misfire detection program 74 a stored in the ROM 74. Theprocess shown in FIG. 9 is a process using the evaluation mapping data76 b.

In the series of processes shown in FIG. 9, the CPU 72 first acquiresthe outside air temperature Ta in addition to the minute rotation timesT30(1), T30(2), . . . T30(24), the rotation speed NE, and the chargingefficiency η (S40 a).

Next, the CPU 72 puts the values obtained by the process of S40 a intothe input variables x(1) to x(27) of the mapping defined by theevaluation mapping data 76 b (S42 a). More specifically, the CPU 72executes the same process as the process of S42 on the input variablesx(1) to x(26), and puts the outside air temperature Ta into the inputvariable x(27).

Next, the CPU 72 calculates the misfire variables Pn(1) to Pn(5)corresponding to the misfire variables P(1) to P(5), by inputting theinput variables x(1) to x(27) into the mapping defined by the evaluationmapping data 76 b (S44 a). More specifically, the mapping defined by theevaluation mapping data 76 b is a neural network having a singleintermediate layer. The neural network described above has coefficientswn(1)ji (j=0 to n, i=0 to 27) and an activation function h1(x) which isan input side non-linear mapping for non-linear conversion of the outputof a linear mapping defined by the coefficients w(1)ji. In the presentembodiment, a hyperbolic tangent is exemplified as the activationfunction h1(x). Incidentally, wn(1)j 0 or the like is a bias parameter,and the input variable x(0) is defined as “1”.

The neural network includes a softmax function for receivingcoefficients wn(2)kj (k=1 to 5, j=0 to n) and prototype variables yn(1)to yn(5) which are outputs of a linear mapping defined by thecoefficient wn(2)kj as inputs, and outputting misfire variables Pn.

Then, the CPU 72 specifies the maximum misfire variable Pn(q) among themisfire variables Pn(1) to Pn(5) (S46 a). Then, the CPU 72 determineswhether or not the maximum misfire variable Pn(q) is any of “1 to 4”(S48 a). Then, when determining that the maximum misfire variable Pn(q)is any one of “1 to 4” (S48 a: YES), the CPU 72 determines that misfireoccurs in the cylinder #q (S50 a). The CPU 72 temporarily ends theseries of processes illustrated in FIG. 9, when the process of S50 a iscompleted or when a negative determination is made in S48 a.

FIG. 10 shows a procedure of a process related to relearning of theevaluation mapping data 76 b according to the present embodiment. Theprocess shown on the left side of FIG. 10 is realized by the CPU 72executing the relearning subprogram 74 b stored in the ROM 74 shown inFIG. 7. The process shown on the right side of FIG. 10 is realized bythe CPU 102 executing the relearning main program 104 a stored in theROM 104. In FIG. 10, the same step numbers are given to the processescorresponding to the processes shown in FIG. 4 for convenience.Hereinafter, the process illustrated in FIG. 10 will be described alongthe time series of the relearning process.

In a series of processes shown on the right side of FIG. 10, when theprocess of S90 is completed, the CPU 102 puts corresponding values intothe input variables x(1) to x(79) of the mapping defined by thehigh-specification mapping data 106 b (S110). That is, assuming that“s=1 to 72”, the CPU 102 puts the minute rotation time T30(s) into theinput variable x(s), puts the rotation speed NE into the input variablex(73), and puts the charging efficiency η into the input variable x(74).The CPU 102 puts the outside air temperature Ta into the input variablex(75), puts the warm-up control variable Vcat into the input variablex(76), puts the coolant temperature THW into the input variable x(77),puts the shift position Vsft into the input variable x(78), and puts theengagement variable Vrc into the input variable x(79). Next, the CPU 102puts the input variables x(1) to x(79) into the mapping defined by thehigh-specification mapping data 106 b, and calculates the misfirevariables Pm(1) to Pm(5) corresponding to the misfire variables Pn(1) toPn(5) (S112).

In the present embodiment, the mapping defined by the high-specificationmapping data 106 b is formed of a neural network in which the number ofintermediate layers is “p” and the activation functions h1 to hp of theintermediate layers are hyperbolic tangents. Here, assuming that m=1, 2,. . . , p, the value of each node in the m-th intermediate layer isgenerated by inputting the output of the linear mapping defined by thecoefficient wm(m) to the activation function hm. Here, n1, n2, . . . ,np are the numbers of nodes in the first, second, . . . , the p-thintermediate layers, respectively. For example, the value of each nodein the first intermediate layer is generated by inputting to theactivation function h1, the output obtained by inputting the inputvariables x(1) to x(79) into a linear mapping defined by coefficientswm(1)ji (j=0 to n1, i=0 to 79). Incidentally, wm(1)j 0 or the like is abias parameter, and the input variable x(0) is defined as “1”.

The neural network includes a softmax function for receivingcoefficients wm(p+1) lr (l=1 to 5, r=0 to np), and prototype variablesym(1) to ym(5) which are outputs of a linear mapping defined by thecoefficients wm(p+1) lr as inputs, and outputting misfire variablesPm(1) to Pm(5).

Then, the CPU 102 determines whether or not the misfire determinationbased on the evaluation mapping data 76 b is an erroneous determination(S96). That is, when the largest one among the misfire variables Pm(1)to Pm(5) and information “q” on the largest one among the misfirevariables Pn(1) to Pn(5) received in the process of S90 do not match,the CPU 102 determines to be an erroneous determination. Specifically,for example, when the largest one among the misfire variables Pm(1) toPm(5) is the misfire variables Pm(1) and the largest one among themisfire variables Pn(1) to Pn(5) is the misfire variables Pn(5),determination is made that an erroneous determination has been made.

When determination is made that an erroneous determination has been made(S96: YES), the CPU 102 executes the processes of S98 and S100. When anaffirmative determination is made in the process of S100, the CPU 102updates the coefficients wn(1)ji, wn(2)kj which are learned parametersof the evaluation mapping data 76 b, using the relearning data 106 a astraining data (S102 a). Then, the CPU 102 operates the communicationdevice 107 to transmit the updated coefficients wn(1)ji, wn(2)kj to thevehicles VC1, VC2, . . . as relearned parameters (S104 a). When theprocess of S104 a is completed, or when a negative determination is madein the processes of S96 and S100, the CPU 102 temporarily ends a seriesof processes illustrated on the right side of FIG. 10.

On the other hand, as shown on the left side of FIG. 10, whendetermination is made that there is the relearned parameter (S82: YES),the CPU 102 receives coefficients wn(1)ji, wn(2)kj (S84 a), and updatesthe evaluation mapping data 76 b stored in the storage device 76 (S86).

When the process of S86 is completed, or when a negative determinationis made in the processes of S78 and S82, the CPU 72 temporarily ends aseries of processes illustrated on the left side of FIG. 10. Asdescribed above, in the present embodiment, when the determinationresult using the practical mapping data 76 a and the determinationresult using the evaluation mapping data 76 b do not match, thedetermination result using the evaluation mapping data 76 b is verifiedby the determination using the high-specification mapping data 106 b.Thereby, the determination result using the evaluation mapping data 76 bcan be verified without relying on the determination by the skilledperson.

Correspondence

The correspondence between the items in the above embodiment and theitems described in the section “SUMMARY” is as follows. The executiondevice corresponds to the CPU 72 and the ROM 74, and the storage devicecorresponds to the storage device 76. The first mapping data correspondsto the practical mapping data 76 a. The second mapping data correspondsto the evaluation mapping data 76 b. The first acquisition processcorresponds to the process of S10 in FIG. 2 and the process of S40 inFIG. 8. The first calculation process corresponds to the processes ofS16 and S18 in FIG. 2 and the process of S42 and S44 in FIG. 8. Thesecond acquisition process corresponds to the process of S40 in FIG. 3and the process of S40 a in FIG. 9. The second calculation processcorresponds to the processes of S42 and S44 in FIG. 3 and the process ofS42 a and S44 a in FIG. 9. The determination process corresponds to theprocesses of S64 and S70. The transmission process corresponds to theprocess of S80. The rotation waveform variables correspond to minuterotation times T30 (1) to T30 (24). The instantaneous speed variablecorresponds to the minute rotation time T30. The second intervalcorresponds to 30° C.A, and the first interval corresponds to 720° C.A.The instantaneous speed variables when a determination is made thatthere is mismatch correspond to T30 (25) to T30 (48), and theinstantaneous speed variables when determination is made that there ismatch correspond to T30 (49) to T30 (72). This corresponds to theprocess of S80 being executed when an affirmative determination is madein the process of S78. The second execution device corresponds to theCPU 102 and the ROM 104. The reception process corresponds to theprocess of S90. The relearning data generation process corresponds tothe processes of S92 to S98 in FIG. 4 and the processes of S110 and S112in FIG. 10. The relearning process corresponds to the process of S102 inFIG. 4 and the process of S102 a in FIG. 10. The display processcorresponds to the process of S92. The validity determination resultcapturing process corresponds to the process of S94. The second storagedevice corresponds to the storage device 106. The third mapping datacorresponds to the high-specification mapping data 106 b. The thirdcalculation process corresponds to the processes of S110 and S112.

Other Embodiments

The present embodiment can be implemented with the followingmodifications. The present embodiment and the following modificationexamples can be implemented in combination with each other within atechnically consistent range.

First Mapping and First Mapping Data

In FIG. 1, data for executing the process of steps S16 and S18 isillustrated as the practical mapping data 76 a, but an applicableembodiment of the present disclosure is not limited to this.

In FIG. 8, a neural network having a single intermediate layer isillustrated as the practical mapping data 76 a, but an applicableembodiment of the present disclosure is not limited to this. Forexample, a neural network having two or more intermediate layers may beused. The activation function h1 is not limited to the hyperbolictangent, but may be, for example, a logistic sigmoid function or ReLU.ReLU is a function that outputs the larger one of the input and “0”. Thenumber of nodes in the output layer of the neural network, that is, thedimension is not limited to “(number of cylinders)+1”. For example, thenumber may be equal to the number of cylinders, and it may be determinedthat there is a misfire in a case where any of the output values exceedsa threshold. Further, for example, the number of cylinders to besubjected to misfire determination based on one output of the neuralnetwork may be one, and the number of nodes in the output layer may beone. In this case, with respect to the output layer, it is desirablethat the range of possible output values is standardized by a logisticsigmoid function or the like.

Practical mapping data is not limited to data defining a neural network.For example, an identification function that outputs numerical values ofdifferent signs depending on the presence or absence of misfire in onecylinder to be subjected to misfire determination may be used. This maybe configured, for example, by a support vector machine.

Second Mapping Data

The evaluation mapping data 76 b as the second mapping data is notlimited to data defining a neural network having one intermediate layer.For example, the second mapping data may be data defining a neuralnetwork having two or more intermediate layers. The activation functionh1 is not limited to the hyperbolic tangent, but may be, for example, alogistic sigmoid function or ReLU. The number of nodes in the outputlayer of the neural network, that is, the dimension is not limited to“(number of cylinders)+1”. For example, the number may be equal to thenumber of cylinders, and it may be determined that there is a misfire ina case where any of the output values exceeds a threshold. Further, forexample, the number of cylinders to be subjected to misfiredetermination based on one output of the neural network may be one, andthe number of nodes in the output layer may be one. In this case, withrespect to the output layer, it is desirable that the range of possibleoutput values is standardized by a logistic sigmoid function or thelike.

The second mapping is not limited to the neural network. For example, anidentification function that outputs numerical values of different signsdepending on the presence or absence of misfire in one cylinder to besubjected to misfire determination may be used. This may be configured,for example, by a support vector machine.

Third Mapping and Third Mapping Data

In the above embodiment, as the third mapping data, data having a largerdimension than the input of the mapping defined by the evaluationmapping data 76 b and a larger number of intermediate layers has beenexemplified, but an applicable embodiment of the present disclosure isnot limited to this. For example, the number of dimensions may be thesame and the number of intermediate layers may be large. This can berealized, for example, by setting the number of intermediate layers tobe two or more while making the input variables the same as thoseexemplified in S42 a. Further, for example, although the number ofdimensions is large, the number of intermediate layers may be the same.

In the above embodiment, the high-specification mapping data 106 b is alearned model in which data transmitted from a plurality of vehiclesVC1, VC2, . . . on which the internal combustion engine 10 of onespecification is mounted is used as training data, but an applicableembodiment of the present disclosure is not limited to this. Forexample, data transmitted from vehicles equipped with various internalcombustion engines having different numbers of cylinders, displacements,or the like may be used as training data. However, in this case, it isdesirable that the specification information such as the number ofcylinders and the displacement is an input variable of the mappingdefined by the high-specification mapping data 106 b. The inputvariables of the mapping defined by the high-specification mapping data106 b are not limited to these, and may include, for example, variablesthat are not used by a skilled person in making a determination. It isnot essential to use a result of determination by the skilled person forat least a part of the teacher data when learning the high-specificationmapping data 106 b.

Instantaneous Speed Variable

The instantaneous speed variable is not limited to the minute rotationtime, which is the time for rotation at the second interval. Forexample, a value obtained by dividing the second interval by the minuterotation time may be used.

Second Interval

The second interval defining the instantaneous speed variable to beinput to the mapping is not limited to 30° C.A. For example, the angularinterval may be smaller than 30° C.A, such as 10° C.A. However, theangular interval is not limited to 30° CA or less, but may be 45° C.A orthe like.

Rotation Waveform Variables as Input to Mapping

In the above embodiment, the minute rotation time T30 at each of aplurality of divided intervals of the rotation angular interval of 720°C.A, which is one combustion cycle, is input to the mapping, but anapplicable embodiment of the present disclosure is not limited to this.For example, 0 to 20, 40 to 60, 80 to 100, 120 to 140, 160 to 180, . . ., or 700 to 720 out of 0 to 720° C.A may be used as a second interval,and the time for the rotation may be used as an input to mapping.

The rotation waveform variable as an input to the mapping is not limitedto the time-series data of the instantaneous speed variable. Forexample, a difference between a pair of instantaneous speed variablesseparated by the appearance interval of the compression top dead centermay be used.

Transmission Process

In the above embodiment, the time-series data of the minute rotationtime T30 for three combustion cycles is transmitted, but an applicableembodiment of the present disclosure is not limited to this. Forexample, time-series data for two combustion cycles of the minuterotation times T30(25) to T30(48) when the determination result usingthe practical mapping data 76 a and the determination result using theevaluation mapping data 76 b do not match and the minute rotation timesT30(49) to T30(72) at the time of transition from the state where adetermination is made that there is mismatch to the state where adetermination is made that there is match may be used.

In the above embodiment, the minute rotation times T30(49) to T30(72) atthe time of transition from the state where a determination is made thatthere is mismatch to the state where a determination is made that thereis match are transmitted, in addition to the minute rotation timesT30(25) to T30(48) when the determination result using the practicalmapping data 76 a and the determination result using the evaluationmapping data 76 b do not match, an applicable embodiment of the presentdisclosure is not limited to this. For example, the time-series data ofthe minute rotation time T30 in the state where a determination is madethat there is match and the time-series data of the minute rotation timeT30 at the time of transition from the state where a determination ismade that there is match to the state where a determination is made thatthere is mismatch may be transmitted.

The time-series data of the minute rotation time T30 at the time oftransition to the state where a determination is made that there ismatch is not limited to the time-series data of one combustion cycle.For example, as described in the section “Second Mapping Data”, when theoutput value by one input is the output of solely the value of themisfire variable of one cylinder, and the input data itself istime-series data of the minute rotation time T30 in a period shorterthan one combustion cycle, the time-series data may be time-series dataof the amount corresponding to the period. However, it is not essentialthat the time-series data of the minute rotation time T30 in the statewhere a determination is made that there is mismatch and the time-seriesdata of the minute rotation time T30 at the time of transition to thestate where a determination is made that there is match are thetime-series data of the minute rotation time T30 in the section havingthe same length.

In the above embodiment, the time-series data of the minute rotationtime T30 for three combustion cycles corresponding to the case where thenumber of times of consecutive determination that there is mismatch ismaximum once per trip is transmitted, but an applicable embodiment ofthe present disclosure is not limited to this. For example, time-seriesdata of all of the minute rotation times T30 during the period when adetermination is made that there are consecutive mismatch, andtime-series data for one combustion cycle of the minute rotation timeT30 at a time of transition from the state where a determination is madethat there is mismatch to the state where a determination is made thatthere is match may be transmitted.

Relearned Parameters

In FIGS. 4 and 10, the relearned parameters, which are updatedparameters, are transmitted to each of the vehicles VC1, VC2, . . . viathe network 110, but an applicable embodiment of the present disclosureis not limited to this. For example, the relearned parameters may betransmitted to the store of the vehicle, and the data in the storagedevice 76 may be updated when each of the vehicles VC1, VC2, . . . areentered the store. Even in such a case, it is possible to furtherevaluate and update the reliability of the evaluation mapping data 76 bupdated by the relearned parameters.

However, it is not essential to provide the vehicle that has providedthe data used for the relearning with the relearned parameters. Theevaluation mapping data 76 b may be updated using the relearnedparameters, and the updated evaluation mapping data 76 b may simply bemounted on a newly developed vehicle. In such a case, it is desirablethat the difference between the displacement of the internal combustionengine mounted on the newly developed vehicle and the displacement ofthe internal combustion engine mounted on the vehicle to which the datafor relearning is transmitted is equal to or less than a predeterminedamount. As in the above embodiment, when the evaluation mapping data isfor outputting the misfire variable according to the probability of theoccurrence of misfire in each cylinder, it is desirable that the numberof cylinders of the internal combustion engine mounted in newlydeveloped vehicles is the same as the number of cylinders of theinternal combustion engine mounted on the vehicle that has sent data forrelearning.

In the processes of FIGS. 4 and 10, after the evaluation mapping data 76b is updated using the relearned parameters, the practical mapping data76 a may be overwritten.

Display Apparatus

In the above embodiment, the display apparatus 112 is disposed in thedata analysis center 100. However, an applicable embodiment of thepresent disclosure is not limited to this, and the display apparatus 112may be disposed in a site different from the site where the storagedevice 106 or the like is disposed.

Relearning Data Generation Process

In FIG. 4, the input data used for calculating the misfire variablesP(j) and Pn(j) calculated using the evaluation mapping data 76 b and therelated data are displayed on the display apparatus 112, thereby askilled person evaluates whether or not this is an erroneousdetermination, but an applicable embodiment of the present disclosure isnot limited to this. For example, the evaluation may be performedautomatically using the high-specification mapping data 106 b. Whenevaluating the misfire variables P(j) and Pn(j) calculated using theevaluation mapping data 76 b, it is not essential to perform evaluationin consideration of data other than the input data used for calculatingthe misfire variables P(j) and Pn(j).

In FIG. 10, it is automatically evaluated whether or not an erroneousdetermination is made, using the high-specification mapping data 106 bbased on the input data used for calculating the misfire variable Pn(j)calculated using the evaluation mapping data 76 b and related data, butan applicable embodiment of the present disclosure is not limited tothis, for example, a skilled person may perform evaluation.

In the process of FIG. 4, for convenience of description, the process ofS92 is executed each time determination is made that there is mismatchbetween the evaluation result using the evaluation mapping data 76 b andthe evaluation result using the practical mapping data 76 a, but anapplicable embodiment of the present disclosure is not limited to this.For example, the process of S92 may be executed when a predeterminedamount of data determined to be mismatched is accumulated. For example,the mismatched data may be accumulated each time, and the process of S92may be executed in response to a request from a skilled person.

In the above embodiment, the validity of the determination result of themapping defined by the evaluation mapping data 76 b is determined byusing a subject having higher accuracy than the mapping defined by theevaluation mapping data 76 b and the practical mapping data 76 a, anapplicable embodiment of the present disclosure is not limited to this.For example, the validity of the determination result of the mappingdefined by the evaluation mapping data 76 b may be determined by amajority decision between the determination result defined by theevaluation mapping data 76 b and the determination result by two or moreother mappings. For example, one of the determination results by the twoor more other mappings may be determined by a skilled person instead ofthe determination result based on the mapping.

Coping Process

In the above embodiment, the process of operating the warning light 90mounted on the vehicle has been exemplified as the notification process,but an applicable embodiment of the present disclosure is not limited tothis. For example, a process of operating the communication device 77 todisplay information indicating that an abnormality has occurred on theuser's portable terminal may be employed.

The coping process is not limited to the notification process. Forexample, it may be a process of operating an operation unit forcontrolling the combustion of the air-fuel mixture in the combustionchamber 18 of the internal combustion engine 10 in accordance withinformation indicating that misfire has occurred.

Misfire Detection System

In the above embodiment, the misfire detection system is configured bythe control device 70 and the data analysis center 100, but anapplicable embodiment of the present disclosure is not limited to this.For example, the misfire detection system may be configured with aportable terminal, in addition to the control device 70 and the dataanalysis center 100. This can be realized, for example, by the portableterminal executing the processing in FIG. 3 and transmitting the resultto the control device 70, in the first embodiment.

Data Analysis Apparatus

A data analysis apparatus may be configured using a portable terminalinstead of the data analysis center 100. This can be realized, forexample, by storing the high-specification mapping data 106 b or thelike in the storage device of the portable terminal and executing theprocess on the right side of FIG. 10 by the portable terminal. In thiscase, solely data on the vehicle VC1 may be transmitted to the portableterminal of the user of the vehicle VC1.

Execution Device

The execution device is not limited to a device that includes the CPU 72(102) and the ROM 74 (104) and executes software process. For example, adedicated hardware circuit (for example, an ASIC) that performs hardwareprocessing on at least a part of the software processing in the aboveembodiment may be provided. That is, the execution device may have anyone of the following configurations (a) to (c). (a) A processing devicethat executes all of the above processes in accordance with a program,and a program storage device such as a ROM that stores the program areprovided. (b) A processing device that executes some of the aboveprocesses in accordance with a program, a program storage device, and adedicated hardware circuit that executes the remaining processes areprovided. (c) A dedicated hardware circuit that executes all of theabove processes is provided. Here, there may be a plurality of softwareexecution devices including the processing device and the programstorage device, and a plurality of dedicated hardware circuits.

Storage Device

In the above embodiment, the storage device 76 in which the evaluationmapping data 76 b and the practical mapping data 76 a are stored and theROM 74 as the storage device in which the relearning subprogram 74 b isstored are separate storage devices, but an applicable embodiment of thepresent disclosure is not limited to this. For example, the storagedevice 106 in which the high-specification mapping data 106 b is storedand the ROM 104 as a storage device in which the relearning main program104 a is stored are separate storage devices, but an applicableembodiment of the present disclosure is not limited to this.

Internal Combustion Engine

In the above embodiment, the in-cylinder injection valve that injectsfuel into the combustion chamber 18 is exemplified as the fuel injectionvalve, but an applicable embodiment of the present disclosure is notlimited to this. For example, a port injection valve that injects fuelinto the intake passage 12 may be used. For example, both a portinjection valve and an in-cylinder injection valve may be provided.

The internal combustion engine is not limited to a spark ignition typeinternal combustion engine, but may be a compression ignition typeinternal combustion engine using light oil or the like as fuel. It isnot essential that the internal combustion engine constitutes the drivesystem. For example, it may be mounted on a so-called series hybridvehicle in which the crankshaft is mechanically connected to theon-vehicle generator and the power transmission from the drive wheel 60is cut off.

Vehicle

The vehicle is not limited to a vehicle in which the device thatgenerates the propulsion force of the vehicle is solely an internalcombustion engine. For example, in addition to the series hybrid vehicledescribed in the section “About internal combustion engine”, a parallelhybrid vehicle or a series-parallel hybrid vehicle may be used.

Others

The drive system device interposed between the crankshaft and the drivewheels is not limited to a stepped transmission, and may be, forexample, a continuously variable transmission.

What is claimed is:
 1. A misfire detection apparatus comprising: astorage device configured to store first mapping data defining a firstmapping for receiving first input data based on a detection value of acrank angle sensor of an internal combustion engine mounted on a vehicleas an input and outputting a value of a first misfire variable that is avariable related to the presence or absence of misfire of the internalcombustion engine, and second mapping data defining a second mapping forreceiving second input data based on the detection value of the crankangle sensor as an input and outputting a value of a second misfirevariable that is a variable related to the presence or absence ofmisfire of the internal combustion engine, the second mapping dataincluding data learned by machine learning; and an execution deviceconfigured to execute a first acquisition process of acquiring the firstinput data, a first calculation process of calculating a value of thefirst misfire variable by using the first input data acquired in thefirst acquisition process as an input of the first mapping, a secondacquisition process of acquiring the second input data, a secondcalculation process of calculating a value of the second misfirevariable by using the second input data acquired in the secondacquisition process as an input of the second mapping, a determinationprocess of determining whether or not a calculation result of the secondcalculation process and a calculation result of the first calculationprocess match, a count process of counting the number of consecutivetimes when a determination is made in the determination process thatthere is mismatch, and a transmission process of transmitting, to anoutside of the vehicle, data on the value of the second misfire variablecorresponding to the maximum number of times among the numbers ofconsecutive times counted in the count process during a predeterminedperiod and data on the second input data used for calculating the valueof the second misfire variable.
 2. The misfire detection apparatusaccording to claim 1, wherein the transmission process includes aprocess of transmitting data on the maximum number of times, in additionto data on a predetermined number of value among values of the secondmisfire variable corresponding to the maximum number of times and dataon the second input data used for calculating each of the values.
 3. Themisfire detection apparatus according to claim 2, wherein: the secondinput data is a rotation waveform variable that is a variable includinginformation about a difference between values of instantaneous speeds atdifferent angular intervals, the instantaneous speeds being rotationspeeds of the crankshaft of the internal combustion engine at theangular intervals smaller than an appearance interval of a compressiontop dead center of the internal combustion engine; the data on thesecond input data used for calculating the value of the second misfirevariable is an instantaneous speed variable which is a variableindicating the instantaneous speed at each of the angular intervalsincluding information about a difference between the instantaneousspeeds indicated by the rotation waveform variable used for calculatingthe value of the second misfire variable when a determination is made inthe determination process that there is mismatch; and the data to betransmitted in the transmission process includes the instantaneous speedvariable in each of the angular intervals generated before and after theangular interval in time series, including information on the differencebetween the instantaneous speeds indicated by the rotation waveformvariable used for the calculation.
 4. The misfire detection apparatusaccording to claim 3, wherein: the angular interval is a secondinterval, the rotation waveform variable is time-series data configuredwith a variable that indicates a difference between the instantaneousspeed variables by the instantaneous speed variable at each of theconsecutive second intervals included in the first interval larger thanthe second interval; and the data to be transmitted in the transmissionprocess includes the instantaneous speed variable in each of theconsecutive second intervals adjacent to the first interval, in additionto the instantaneous speed variable at each of the second intervalsincluded in the first interval when a determination is made in thedetermination process that there is mismatch.
 5. The misfire detectionapparatus according to claim 3, wherein the data to be transmitted inthe transmission process includes the instantaneous speed variable whena determination is made in the determination process that there ismatch, in addition to the instantaneous speed variable relating to therotation waveform variable used for calculating the value of the secondmisfire variable when a determination is made in the determinationprocess that there is mismatch.
 6. The misfire detection apparatusaccording to claim 4, wherein the data to be transmitted in thetransmission process includes the instantaneous speed variable when adetermination is made in the determination process that there is match,in addition to the instantaneous speed variable relating to the rotationwaveform variable used for calculating the value of the second misfirevariable when a determination is made in the determination process thatthere is mismatch.
 7. The misfire detection apparatus according to claim5, wherein the data to be transmitted in the transmission processincludes the instantaneous speed variable when a determination is madein the determination process that there is mismatch, and theinstantaneous speed variable in a state where a determination is madethat there is match at a time of transition from a state where adetermination is made that there is mismatch to the state where adetermination is made in the determination process that there is match.8. The misfire detection apparatus according to claim 6, wherein thedata to be transmitted in the transmission process includes theinstantaneous speed variable when a determination is made in thedetermination process that there is mismatch, and the instantaneousspeed variable in a state where a determination is made that there ismatch at a time of transition from a state where a determination is madethat there is mismatch to the state where a determination is made in thedetermination process that there is match.
 9. The misfire detectionapparatus according to claim 1, wherein the execution device sets a timeinterval between a pair of end times adjacent to each other in timeseries among end times of travel of the vehicle to the predeterminedperiod, and executes the transmission process at the end time of thetravel of the vehicle.
 10. The misfire detection apparatus according toclaim 2, wherein the execution device sets a time interval between apair of end times adjacent to each other in time series among end timesof travel of the vehicle to the predetermined period, and executes thetransmission process at the end time of the travel of the vehicle. 11.The misfire detection apparatus according to claim 3, wherein theexecution device sets a time interval between a pair of end timesadjacent to each other in time series among end times of travel of thevehicle to the predetermined period, and executes the transmissionprocess at the end time of the travel of the vehicle.
 12. The misfiredetection apparatus according to claim 4, wherein the execution devicesets a time interval between a pair of end times adjacent to each otherin time series among end times of travel of the vehicle to thepredetermined period, and executes the transmission process at the endtime of the travel of the vehicle.
 13. The misfire detection apparatusaccording to claim 5, wherein the execution device sets a time intervalbetween a pair of end times adjacent to each other in time series amongend times of travel of the vehicle to the predetermined period, andexecutes the transmission process at the end time of the travel of thevehicle.
 14. The misfire detection apparatus according to claim 6,wherein the execution device sets a time interval between a pair of endtimes adjacent to each other in time series among end times of travel ofthe vehicle to the predetermined period, and executes the transmissionprocess at the end time of the travel of the vehicle.
 15. The misfiredetection apparatus according to claim 7, wherein the execution devicesets a time interval between a pair of end times adjacent to each otherin time series among end times of travel of the vehicle to thepredetermined period, and executes the transmission process at the endtime of the travel of the vehicle.
 16. The misfire detection apparatusaccording to claim 8, wherein the execution device sets a time intervalbetween a pair of end times adjacent to each other in time series amongend times of travel of the vehicle to the predetermined period, andexecutes the transmission process at the end time of the travel of thevehicle.
 17. A misfire detection system comprising: a storage deviceconfigured to store first mapping data defining a first mapping forreceiving first input data based on a detection value of a crank anglesensor of an internal combustion engine mounted on a vehicle as an inputand outputting a value of a first misfire variable that is a variablerelated to the presence or absence of misfire of the internal combustionengine, and second mapping data defining a second mapping for receivingsecond input data based on the detection value of the crank angle sensoras an input and outputting a value of a second misfire variable that isa variable related to the presence or absence of misfire of the internalcombustion engine, and the second mapping data including data learned bymachine learning; a first execution device mounted on the vehicle, thefirst execution device being configured to execute a first acquisitionprocess of acquiring the first input data, a first calculation processof calculating a value of the first misfire variable by using the firstinput data acquired in the first acquisition process as an input of thefirst mapping, a second acquisition process of acquiring the secondinput data, a second calculation process of calculating a value of thesecond misfire variable by using the second input data acquired in thesecond acquisition process as an input of the second mapping, adetermination process of determining whether or not a calculation resultof the second calculation process and a calculation result of the firstcalculation process match, a count process of counting the number ofconsecutive times when a determination is made in the determinationprocess that there is mismatch, and a transmission process oftransmitting, to an outside of the vehicle, data on the value of thesecond misfire variable corresponding to the maximum number of timesamong the numbers of consecutive times counted in the count processduring a predetermined period and data on the second input data used forcalculating the value of the second misfire variable; and a secondexecution device outside the vehicle, the second execution device beingconfigured to execute a reception process of receiving the datatransmitted in the transmission process, a relearning data generationprocess of generating relearning data that is data for relearning thesecond mapping, based on the data received in the reception process, anda relearning process of relearning the second mapping data, based on therelearning data generated in the relearning data generation process. 18.The misfire detection system according to claim 17, wherein therelearning data generation process includes a display process ofdisplaying the data received in the reception process on a displayapparatus, a validity determination result capturing process ofcapturing information on whether or not there is an error in the outputvalue of the second mapping, and a process of generating data forupdating the second mapping data, based on the information captured inthe validity determination result capturing process.
 19. The misfiredetection system according to claim 17, wherein: the storage deviceincludes a first storage device mounted on the vehicle and a secondstorage device outside the vehicle; the second storage device isconfigured to store third mapping data defining third mapping whichreceives third input data based on the detection value of the crankangle sensor as an input and outputs a value of a third misfirevariable, which is a variable related to the presence or absence ofmisfire in the internal combustion engine; and the relearning datageneration process includes a third calculation process of inputting thedata received in the reception process into the third mapping andcalculates the value of the third misfire variable, and a process ofgenerating data for updating the second mapping data, based on whetheror not the calculation result of the third calculation process and thecalculation result of the second calculation process match.
 20. A dataanalysis apparatus comprising an execution device outside a vehicle, theexecution device being configured to execute a reception process ofreceiving data on a value of a misfire variable in an internalcombustion engine corresponding to the maximum number of times among thenumbers of consecutive times counted in a predetermined period,transmitted from the vehicle, and data on input data based on adetection value of a crank angle sensor used to calculate the value ofthe misfire variable, a relearning data generation process of generatingrelearning data that is data for relearning a mapping, based on the datareceived by the reception process, and a relearning process ofrelearning the mapping data, based on the relearning data generated inthe relearning data generation process, wherein: the mapping is amapping defined so as to receive input data based on the detection valueof the crank angle sensor as an input and to output a value of themisfire variable that is a variable relating to the presence or absenceof misfire in the internal combustion engine.